Case based Reasoning as a Tool to Improve Microcredit

Mohammed Jamal Uddin, Giuseppe Vizzari and Stefania Bandini

Complex Systems and Artificial Intelligence Research Center, University of Milano-Bicocca,

Viale Sarca 336/14, 20126, Milan, Italy

Keywords: Microcredit, Case-based Reasoning, Knowledge Artifacts.

Abstract: This paper will discuss the possibility to adopt the Case-Based Reasoning approach to improve microcredit

initiatives. In particular, we will consider the Kiva microcredit system, which provides a characterisation

(rating) of the risk associated to the field partner supporting the loan, but not of the specific borrower which

would benefit from it. We will discuss how the combination of available historical data on loans and their

outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated to a

loan request (exploited to actually rate past cases and therefore bootstrap the CBR system), can be used to

provide the end-users with an indication of the risk rating associated to a loan request based on similar past

situations. From this perspective, the case-base and the codified knowledge about how to evaluate risks

associated to a loan represent two examples of knowledge IT artifacts.

1 INTRODUCTION

Microfinance and microcredit represent innovative

and effective poverty alleviation instruments,

recently conceived and implemented to support the

creation of income-generating and sustainable

activities in developing countries. Although these

initiatives often require basic and relatively trivial

interventions of digitization and automation of

activities, the new scenario represents an interesting

area of research for disciplines like economics but

also for computer science. In particular, this paper

discusses the possibility to adopt the Case-Based

Reasoning approach (Aamodt and Plaza, 1994) to

improve microcredit initiatives. More precisely, we

will consider the Kiva microcredit system, which

provides a characterisation (rating) of the risk

associated to the field partner supporting the loan,

but not of the specific borrower which would benefit

from it. We will discuss how the combination of

available historical data on loans and their outcomes

(structured as a case base) and available knowledge

on how to evaluate the risk associated to a loan

request (exploited to actually rate past cases and

therefore bootstrap the CBR system), can be used to

provide the end-users with an indication of the risk

rating associated to a loan request based on similar

past situations. From this perspective, the case-base

and the codified knowledge about how to evaluate

risks associated to a loan represent two examples of

knowledge IT artifacts (Cabitza and Locoro, 2015);

(Salazar et al., 2008). The paper breaks down as

follows: the following sections will present a state of

the art in microfinance and microcredit sectors, then

Section 3 will introduce Kiva and its workflow,

highlighting where and how the proposed system

can be set. Section 4, finally, will describe the

overall approach and the current state of

development of the proposed system. Conclusions

and future developments will end the paper.

2 MICROFINANCE

Microfinance is regarded as an innovative and

effective poverty-alleviation tool to help the

unbanked poor people, especially in developing

countries, aiming to create income-generating

activities (Milana and Ashta, 2012); (Hamada,

2010); (Amin, 2008). In developing countries,

economic managers have been challenged by and

continue to challenge issues like employment

generation, poverty reduction and sustainable

development that microfinance is dedicated to

deliver. It still works as a critical approach against

poverty and financial exclusion even facing some of

its recent crises and the resulting criticism (Isa et al.,

2011). Microfinance provides the underserved poor

466

Uddin, M., Vizzari, G. and Bandini, S..

Case based Reasoning as a Tool to Improve Microcredit.

In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 3: KMIS, pages 466-473

ISBN: 978-989-758-158-8

access to financial services that help alleviate

poverty through encouraging income-generating

activities, empowering, and enhancing security

which are the priority programs of World Bank

proposed in a set of strategies for fighting against

poverty in 2000 (World Attacking Development,

2000).

Since its beginning in the early 1970s,

microfinance has remarkable performance with

strong growth for which it has been positively

acknowledged by the stakeholders from all corners

of the world, and especially by the award in 2006 of

the Nobel Peace Prize to Muhammad Yunus, the

founder of microcredit modeled as Grameen Bank

(Pompa et al., 2012). Traditionally, the need, small

amount of loan without collateral, of the poor people

is not served by the formal financial institutions.

Also, such services are out of their reach due to the

complicated application procedure, high interest

rates, and long admission processing. Making the

poor people access to financial services, especially

microcredit, and sustaining the good repayment rate

without collateral by designing an appropriate

institutions are the significant contributions of

microfinance. Some of the successful models of

microcredit are Grameen Bank, BRAC, ASA in

Bangladesh, SKS Microfinance Ltd. in India, Bank

Rakyat Indonesia (BRI) in Indonesia. Current

statistics of Grameen Bank (October 2014) show

that it has 8.6 million borrowers, of which 97% are

women for cumulative loan portfolio of $16.1 billion

with outstanding balance for $1.1 billion and total

deposit of $2.0 billion, of which 62% is borrower's

deposit through 2,568 branches in 81,379 villages

(2011), covering more than 97% of the total villages

in Bangladesh employing 21,851 (2013) existing

employees. Its repayment rate is 97.83%. Among

over 3,500 Microfinance Institutions (MFIs) across

the world, the latest data from the 1,252 financial

services providers listed on MIX Market shows that,

as of December 2012, they reached 91.4 million low

income clients for an $81.5 billion portfolio.

However, with 2.5 billion ‘unbanked’ people

through greater financial inclusion -a new direction,

the challenge of financial access remains.

Microfinance, in broader sense, is a provision of

basic financial services accessible to poor people

who are usually denied by traditional banking

system. Such services are small loans, savings,

insurance, and money transfer facilities (Wrenn,

2007); (Elahi and Rahman 2006). Self-help Group

(SHG) and Joint Liability Group (Grameen model

and its variants) are two common credit delivery

models in microfinance (Nayak, 2010). Recently,

MFIs have been providing loans to individuals who

need a larger size loan and who does not match with

the other members in a group (Milana and Ashta,

2012); (Hamada, 2010). Such loans are provided

especially for business and /or development

purposes based on the personal creditworthiness and

the capacity to produce any guarantee (like personal

guarantor from friends/relatives, post-dated cheques,

collateral security) of the microcredit borrower. In

addition, MFIs also consider the borrower's technical

skills in business and his/her reputation in peers and

society. It is like conventional (quasi) lending but

with flexible terms (Islam et al., 2012); (Nayak,

2010).

Microcredit in the early 1970s was the first

revolution of microfinance targeted the unbanked

rural people accessible to small loans without

collateral. Such tiny amount of $100 loans was

provided to the poor, especially women in the

village who were denied by the traditional banking

system. In this microcredit, the borrower's capacity

and will to make regular savings and repay the loan

with interest in a short time were the core issues to

be successful. Non-profit based NGOs were the

microcredit providers who argued that poverty is not

a creation of any individual or social choices, but is

the unwanted result of government or market

failures depriving the poor of their rights to access

financial services. The initial development was from

the viewpoint of microcredit bank (supplier/lender's

perspective) targeting reach out and searching for

operational cases. Satisfactory repayment rate of

microcredit program was very crucial to make it

successful and sustainable. The important

requirement of collateral was replaced by ‘group

formation’ and ‘mandatory savings’ that worked as

guarantee against default (Milana and Ashta, 2012).

Eventually, such innovation became 'product-

centered' services, rather than the required services

centered on the real needs of the microcredit

borrowers. Group lending with joint liability

positively impacts on screening, monitoring, and

state verification (Hermes and Lensink, 2007). It

also helps reduce the problem of asymmetric

information in borrower selection, loan monitoring,

auditing, and enforcement (Ghatak and Guinnane,

1999); (Paal and Wiseman, 2011). Repayment in

group lending is influenced by religious intensity (Al

Azzam et al., 2012) as well as trust coming from

social conformity and reciprocity (Attanasio et al.,

2012). In addition to joint liability of the group

lending model, there are some other good aspects

like peer monitoring, auditing, and sequential

lending which make this model elegant (Chowdhury,

Case based Reasoning as a Tool to Improve Microcredit

467

2005). However, group lending works better than

individual lending only if peer monitoring costs are

lower than those of lender monitoring (Cason et al.,

2012). Moreover, as individual members within the

group grow at different rates and is penalized for

default members, the one-size-fits-all standard loan

will not work any longer, and individual lending will

need to replace group lending (Besley and Coate,

1995). Such quasi conventional lending- individual

lending with flexible terms are emerging in the

direction of peer-to-peer online lending platform. In

customer-centered finance, it has to start from

financial services that customers really need. Such

services like the provision of capital, credit, and

insurance making a series of social intermediations

create an organized social voice rendering demands

to public institutions and policy makers, building

self-confidence (Elahi and Rahman, 2006).

Capturing the whole movement of recent

developments in microfinance is beyond the scope

of this study, but our study tries to focus on the new

direction of microfinance, web-based peer-to-peer

microcredit (P2P microcredit model).

3 MICROCREDIT

One of the trends of today's microfinance operations

is the adaptation and transformation of their business

models from paper-based to digital or automation

due to the continuous competitive pressure to

balance between outreach and operational

sustainability. Such trend is connecting the

developed world to the developing countries in

different dimensions making some parts of

microfinance business easy and globally accessible

in one hand, but throwing some challenges to the

same market on the other hand. Peer-to-Peer (P2P)

platforms are emerging with such challenges to them

who are still employing paper-based workflows, but

with the opportunities through web-based or the

Internet models making this line of business more

accessible and comfortable to all including

individual lenders. Many P2P platforms have

developed to spread the microcredit to individual

lenders in the developed world aiming to link

Western investors to the sector. Among the leading

P2P platforms some of the most relevant are Kiva,

MYC4, Zidisha, myELEN, Opportunity

International, and the Microloan Foundation.

Another web-based microlending model with unique

features is United Prosperity that is non-profit

platform uses its lending money to provide

guarantee to local banks that provide microloans to

micro-entrepreneurs. Such guarantee from United

Prosperity enables the banks greater leverage

(usually loan amount is double of the guarantee)

than traditional micro-credit. Also, this model unlike

others facilitates the micro-credit borrowers to

develop a relationship or a credit history with local

banks by which the borrowers become independent

in the long term. The prime advantages of these P2P

models are lower operational costs, affordable

interest burden to borrowers, easy access of Western

individual lenders/investors who made the source of

lending fund's portfolio well diversified, globally

accessible platforms which are accessible by any

poor borrowers specially from most of the

developing countries. In 2009, US-based Zidisha

became the first P2P microlending model to connect

lenders and borrowers directly worldwide without

local intermediaries. Based on wider coverage of

both borrowers as well as lenders another US-based

P2P model, Kiva has significant lending for more

than $643 million from 1.2 million plus lenders in

84 different countries across the world. Although it

is already mentioned about the benefits of such web-

based models in microfinance activities, these P2P

models have some problems like disclosure

regarding borrower's or applicant's information, risk

rating in borrower selection, methodologies for

providing loans through local partners/

intermediaries and interest rate computation (use of

flat rates), recovery of loans in case of default etc.

Among them, the problem with the borrower/ loan

applicant's information is critical to the web-based

lenders to remain active in such platforms and to

sustain them in the long term in the promotion of

noble goal, reducing global poverty. Moreover, it is

serious because no individual credit risk rating is

provided directly or indirectly by the field partners

or by such lending platforms resulting bearing the

default risk lies absolutely with the lenders who

ultimately refinance the field partners. To address

this problem, we have chosen the leading model,

Kiva to represent the borrower / loan applicant's

profile in a scientific manner which is not only solve

the problem of borrower's information in Kiva but

also in other models that have the same problem in

this sector.

4 Kiva AND ITS WORKFLOW

Kiva, founded in 2005, is a non-profit organization

with the mission to connect people through lending

to alleviate poverty. Its lending mechanism is

different (Internet-based) from traditional lending

KITA 2015 - 1st International Workshop on the design, development and use of Knowledge IT Artifacts in professional communities and

aggregations

468

technology (group based brick-and-mortar model)

and even from other web-based lending models in

microfinance sector. It acts as an intermediary

(online platform) to provide people with the

opportunity to lend interest-free small amounts of

money to underprivileged entrepreneurs via the

Internet to microfinance organizations in the

developing world. Kiva allows its users (lenders) to

lend the money to borrowers (entrepreneurs) through

its field partners (Microfinance Institutions-MFIs).

Figure 1: Overall Kiva workflow.

It follows, Figure 1, the following steps: (1a) the

borrower meets with the Field Partner and requests a

loan. The Field Partner, if certain criteria are met,

disburses a loan to the borrower (1b). After loan

disbursement, the Field Partner uploads the loan

request to Kiva (2a), it's reviewed by a team of

volunteer editors and translators and then published

on Kiva.org (2b). Kiva lenders fund the loan request

(2c), and Kiva sends the funds to the Field Partner

(2d). The borrower, later on, makes repayments (3a)

and the Field Partner sends funds owed to Kiva (3b).

Kiva repays the principal amount to its lenders (3c).

The lenders can make another loan, donate to Kiva,

or withdraw the money to their PayPal account.

Such loan can be made individually or in teams by

the lenders (one to one, many to one, and many to

many) (Choo et al., 2014).

As briefly discussed before, the users viewing

loan request descriptions have an indication of the

risk rating associated to the Field Partner, based on

historical data of loans managed by that

organization. However, no indication on the risk

associated to the specific loan request is provided, as

shown in Figure 2. Selecting a borrower is a

challenging task to online microcredit lenders as

individual borrowers’ profiles do not provide any

risk rating on the site/platform except the

microfinance intermediaries’ aggregate risk

indicators (depicted on the right bottom corner in

Fig.2) based on the actual repayment of previous

borrowers managed by the same field partner. This

information can surely suggest good assessment and

management capabilities of the field partner, but it is

essentially unrelated to the current borrow request.

Figure 2: An example of Kiva loan request description.

Moreover, the sites merely keep typical advices

for lenders/end users to diversify their portfolios

through lending to more than one borrower via

different field partners as well as in different

countries and/or sectors. However, an indication of

the borrower’s risk, which is missing on the models

(indicated in Fig.2), remains critical to the aggregate

or individual lenders in the sites.

5 CBR FOR LOAN RISK RATING

IN Kiva

The Case–Based Reasoning approach is based on the

reasoning by analogy method (i.e. similar problems

have similar solutions), summarized in the well

known 4R’s cycle by Aamodt and Plaza (1994)

described in Figure 3: a description of the new

problem is given, then it is compared to the

description of similar problems already solved and

Case based Reasoning as a Tool to Improve Microcredit

469

stored in the case base according to a similarity

algorithm. The most similar problem description is

then retrieved and its solution is reused as a first

attempt to solve the new problem without starting

from scratch. If the reused solution doesn’t fit the

problem a revise step can be applied to adapt it.

Finally, the new problem description and its

(possibly revised) solution are retained in the case

base. Of course this approach requires the definition

of (i) a case structure, comprising a description of

the situation, an adopted solution and an outcome,

and (ii) a proper similarity metric supporting the

retrieval of cases that are relevant to the one at hand.

This problem solving paradigm is suitable to deal

with domains whose problem solving methods have

not been fully understood and modelled, but in

which experiential and episodic knowledge is

instead present. In fact, within this paradigm it is not

necessary to elicit and represent the knowledge

required to construct a solution from the description

of the current problem, but it is rather necessary to

have an idea of how to compare two situations, two

cases, and rate their degree of similarity.

Figure 3: CBR Cycle.

Provided that the number of past cases is

sufficiently covering the range of possibilities, it is

plausible to think that the solution to a past situation

sufficiently similar to the one at hand will be a

useful support to the definition of a line of work for

the current problem. Knowledge elicitation and

representation phases in the definition, design and

implementation of a CBR system are therefore

focused on the definition of a proper structure for the

case description (as suggested above, composed of a

description, solution and outcome parts) and also of

a proper similarity metric. The most knowledge

intensive phase of the CBR cycle is about the

adaptation of the past case solution to the present

situation (Manzoni et al., 2007): it is not unusual that

this phase is actually delegated to the human expert

(the so-called null adaptation approach) due to the

lack of sufficient knowledge to systematically

perform this kind of activity.

The main issue with the application of this

approach to the present problem is certainly not the

lack of data. In fact, Kiva makes available all the

information associated to past loan requests and to

the actual repayments made by the borrowers. All

the information necessary to define a case

description is available (for sake of space we just

report an ER diagram of the defined schema in

Figure 4: it contains information about the borrower,

her location, the planned activity to be funded and

the field partner), and also the final outcome is

known. In particular, the information about the

planned and actual repayments and their timing are

present, where the comparison among the two types

of records (planned and actual payment) provides

useful indication on the fact that the borrower paid

back in time (or even early), or he/she rather had

problems in respecting the planned schedule. Of

course no actual risk rating is present and therefore

all cases would be missing the solution part. To

solve this cold boot problem, we decided to adopt a

strategy depicted in Figure 5: we chose to select a

reasonable number of past loans that are sufficiently

representative of all the countries, economical

sectors for the funded activities, kind of borrowers,

and actually rate them (filling thus the solution part

of the case) employing expert rules for rating the

risk associated to loan requests in developing

countries, coded into a spreadsheet. This activity

cannot, as of this moment, be completely automated

due to the need to interpret elements of the borrower

description written in natural language and not

structured in fields of a database. Moreover, the

above mentioned rules are not completely

formalized and the experts sometimes actually

manually modify the results of their direct

application to define the risk rating. Expert rules are

based on their opinion regarding objective assess-

ment assessment of subjective judgement in order to

arrive at the credit risk score for rating of borrower's

risk. The obtained credit risk score will be required

to be validated in holdout sample.

The knowledge elicitation activity carried out in

order to define these rules was characterised by

several interviews with experts in risk assessing in

the microcredit context. These experts are actually

proficient in the usage of spreadsheets and the

interviewer was actually able to involve them in the

creation of this support tool. From this perspective,

this spreadsheet can be considered as a form of

socially situated ITKA (Cabitza and Locoro, 2015),

KITA 2015 - 1st International Workshop on the design, development and use of Knowledge IT Artifacts in professional communities and

aggregations

470

Figure 4: ER diagram of information related to a loan request as extracted by the Kiva XML data.

since it is aimed at providing a form of decision sup

port but in the present form it is more an agile way

to share and discuss rules and points of views on

how to perform the risk-rating activities.

As a result of this activity, a proper case base is

achieved and the actual CBR system can be used.

The loan similarity metric was actually defined also

exploiting the above mentioned rules for risk rating,

that highlight what parts of a loan request

description are most significant in determining the

overall risk and that therefore are also more relevant

for deciding about the similarity of cases. The

similarity metric has been designed on the basis of

profiling/characterizing attributes of borrower’s

success or failure in loan delinquency or not. This

will be identified through the use of multiple

discriminant analysis, which will allow to

discriminate between success and failure or loan

delinquency and not as far as possible minimizing

the error of misspecification. Whereas the

spreadsheet supporting manual risk-rating can be

considered as a socially situated ITKA, the CBR

system, in its final form, will instead be a typical

example of representational ITKA (Cabitza and

Locoro, 2015), since the knowledge structure,

resulting from a careful information and knowledge

modelling phase, will be mostly fixed, although the

system will be provided with a form of incremental

learning, inherited by the CBR approach. Although

the system is characterised by a certain degree of

flexibility, for instance in the weights and even the

form of the similarity metric, changing the structure

of the case would represent a problem for a potential

inclusion of this system in Kiva’s workflow. The

experts involved in this study actually suggested that

having the possibility to manage in a structured way

information generally stored as text within the

borrower description field would be actually useful

for having a more refined similarity metric, but for

the time being we decided to preserve the actual data

structure adopted by Kiva’s database and evaluate

the achieved result before proposing such a

problematic change.

Finally, it must be emphasized that the case base

and even just the database of past loans actually

represent objects of interest for researchers still

involved in the study of microcredit but also in the

perception of risk associated to loans. From this

perspective, this does not represent an ITKA per se,

but we are involved in research collaborations that

might lead to the creation of a proper form of ITKA

enclosing this database for collaborative research

activities, in a socially situated ITKA perspective.

Case based Reasoning as a Tool to Improve Microcredit

471

CBR system

Kiva XML

data

XQuery

processing

Past loans

Knowledge-

Based Risk

Rating

Past loan

cases

Expert

Rules for

Risk Rating

Loan

request

similarity

metric

Figure 5: workflow for setting up the CBR system from

initial Kiva data.

6 CONCLUSIONS

The paper has presented a CBR tool for improving

microcredit system (Kiva), in particular for

providing a loan request risk rating based on past

loans that are most similar to the new one to be

published on the microcredit web site. The system

has been developed and a strategy to solve the cold

boot problem has been devised and implemented: as

of this moment, the case base is being populated to

better cover the variety of the potential loan

requests, and then we will proceed with a

quantitative evaluation of the CBR system

effectiveness.

This tool can be deployed to filed partners (1a in

Fig.1) or alternatively to Kiva Systems (2b in Fig.1).

The field partners can use this tool to assess/rate the

new applicants (who will make loan requests to the

field partners) and based on the rating they can also

provide suggestions to the applicants for how to

make their businesses more appealing, competitive

for the loans and also, hopefully, more successful. In

case of Kiva Systems, they can adopt/align this tool

in their existing systems and thereby incorporate the

rating in borrower’s description space. Such kind of

incorporation will definitely help the end

users/lenders understand the risk category of the

borrowers. As a result, the lenders will be able to

diversify the lending risk of their lending portfolios.

ACKNOWLEDGEMENTS

The authors would like to thank Andrea Sedini for

his support in the development of the CBR system

and on the conversion from Kiva’s XML format to

the adopted database system. Moreover, the authors

also thank Dr. Kazi Ahmed Nabi, Professor of

Finance and Banking, University of Chittagong,

Professor Dr. M. A. Baqui Khalily, Executive

Director of Institute of Microfinance (InM), Dr.

Mahmood Osman Imam, Professor of Finance,

University of Dhaka, and Professor Dr. M. Jahangir

Alam Chowdhury, Professor of Finance & Executive

Director of Center for Microfinance and

Development, University of Dhaka for their

contributions in defining the risk rating rules, Mr.

Md. Fazlul Kader, Deputy Managing Director of

Palli Karma-Sahayak Foundation (PKSF) and Mr.

Md. Shahid Ullah, Executive Director of

Development Initiative for Social Advancement

(DISA) in understanding the current practices of

borrower’s risk assessment.

REFERENCES

Aamodt, A., Plaza, E. (1994). Case-based reasoning:

Foundational issues, methodological variations, and

system approaches. AI Communications (7) 39–59

Al-Azzam Md, Hill R.C, Sarangi S. (2012). Repayment

performance in group lending: evidence from Jordan.

Journal of Development Economics 97: 404–414.

Attanasio O, Barr A, Cardenas JC, Genicot G, Meghir C.

(2012). Risk pooling, risk preferences, and social

networks. American Economic Journal: Applied

Economics 4(2): 134–167.

Amin, N. (2008). Enabling the Expansion of Microfinance

Using Information and Communication Technologies,

3658–3680

Besley, T., & Coate, S. (1995). Group lending, repayment

incentives and social collateral. Journal of

Development Economics, 46, 1–18.

Cabitza, F., & Locoro, A. (2015). “Made with

Knowledge”: disentangling the IT Knowledge Artifact

by a qualitative literature review. In Knowledge

Discovery, Knowledge Engineering and Knowledge

Management 5th International Joint Conference, IC3K

2014, Roma, Italy, October 21-24, 2014. (Vol.

Forthcoming, pp. 64−75). Springer.

Cason, T. N., Gangadharan, L., & Maitra, P. (2012).

Moral hazard and peer monitoring in a laboratory

microfinance experiment. Journal of Economic

Behavior and Organization, 82(1), 192–209.

Choo, J., Lee, C., Lee, D., & Park, H. (2014).

Understanding and Promoting Micro-Finance

Activities in Kiva. org.

Chowdhury, P. R. (2005). Group-lending: Sequential

financing, lender monitoring and joint liability.

Journal of Development Economics, 77, 415–439.

doi:10.1016/j.jdeveco.2004.05.005

Elahi K.Q, Rahman M.L. (2006). Micro-credit and micro-

finance: functional and conceptual differences.

Development in Practice 16(5): 476–483.

KITA 2015 - 1st International Workshop on the design, development and use of Knowledge IT Artifacts in professional communities and

aggregations

472

Ghatak, M., & Guinnane, T. W. (1999). The economics of

lending with joint liability: Theory and practice.

Journal of Development Economics, 60(May), 195–

228. doi:10.1016/S0304-3878(99)00041-3

Hamada, M. (2010). Financial Services to the Poor: an

Introduction to the Special Issue on Microfinance. The

Developing Economies, 48(1), 1–14.

Hermes, N., & Lensink, R. (2007). The empirics of

microfinance: What do we know? Economic Journal,

117, 1–10.

Isa, Q. A., Ashfaq, Y., & Haq, A. (2011). What Makes a

Microfinance Apex Work ?

Islam, J. N., Sciences, P., Mohajan, H. K., & Datta, R.

(2012). Aspects of Microfinance System of Grameen

Bank of Bangladesh, International Journal of

Economic Research, (August), 76–96.

Manzoni, S., Sartori, F., Vizzari, G. (2007). Substitutional

Adaptation in Case-Based Reasoning: A General

Framework Applied to P-Truck Curing. Applied

Artificial Intelligence 21(4&5): 427-442 (2007)

Milana, C., & Ashta, A. (2012). Developing microfinance:

A survey of the literature. Strategic Change, 21(7-8),

299–330.

Nayak Abhay. (2010). Credit Delivery Methodologies

used by Microfinance Institutions, India.

Paal, B., & Wiseman, T. (2011). Group insurance and

lending with endogenous social collateral. Journal of

Development Economics, 94(1), 30–40.

Pompa, A., Daley-harris, S., Asia, S., & Bn, E. U. R.

(2012). Microfinance Barometer 2012 individual

lending.

Salazar-Torres, G., Colombo, E., Da Silva, F. C., Noriega,

C. A., & Bandini, S. (2008). Design issues for

knowledge artifacts. Knowledge-based systems, 21(8),

856-867.

World Attacking Development. (2000).

Wrenn, E. (2007). Perceptions of the Impact of

Microfinance on Livelihood Security. Research and

Perspectives on Development Practice. Dublin,

Ireland.

Case based Reasoning as a Tool to Improve Microcredit

473